[Hiring] Robotics Researcher — Manipulation @Omakase Robotics

Role Description

You will train the models that make our robots' hands work. Today that means post-training VLA policies (π0.5-class, ACT) on our own teleoperation data and deploying them onto real robots doing real jobs in Japan. Next it means pre-training a cross-embodiment manipulation foundation model on our proprietary dataset.

You own the loop end to end:

• Data curation
• Training
• Evaluation
• Real-robot deployment
• Failure analysis
• Back to data

We do not have a separate team that "puts the model on the robot." That handoff is where robot learning usually goes to die.

What you would actually work on

• Action-space representation: Our end-effector-space policies are systematically weaker on rotation than on translation. Until the representation is fixed, EE-space deployment is blocked. This is an open problem we would want your opinion on in the interview.
• Cross-embodiment transfer: We collect human hand trajectories and retarget them to the robot. How much of that transfers, and in which representation, is not yet settled by our own measurements.
• Evaluation that predicts real-robot success: Open-loop MSE on held-out episodes is cheap and weakly correlated with what happens on the robot. We build sim-based harnesses and structured real-robot trials, and we would like them to disagree less.
• Making failures legible: When a policy misses a grasp, the useful question is which part of the pipeline was wrong — the data, the representation, the training, or the hardware. We invest in being able to answer that.

Responsibilities

• Train and post-train VLA / imitation-learning policies (diffusion and flow-matching action heads, ACT, π-class models) on real teleoperation data
• Design and run the pre-training effort for our cross-embodiment manipulation foundation model
• Build rigorous evaluation: open-loop metrics on held-out real data, sim-based eval harnesses, structured real-robot trials
• Own multi-node distributed training (we operate H100/H200 clusters) — throughput, dataloading, checkpointing
• Work with the data team on dataset schemas, quality gates, and retargeting (EE-space cross-embodiment representations)
• Deploy policies to real robots with the OS team and drive failure analysis back into data and training

Qualifications

• MS/PhD in ML, robotics, or CS — or an equivalent track record that speaks for itself
• 3+ years training large neural models, including at least one substantial robot-learning or VLA project you can walk us through in depth — what failed, what you measured, what you would do differently
• Demonstrated experience with imitation learning / VLA architectures (π0-class, OpenVLA, RT-class, ACT, diffusion policies). Fine-tuning through an API does not qualify
• Strong PyTorch or JAX; comfortable owning multi-GPU / multi-node training runs end to end
• Experience evaluating policies beyond loss curves: held-out real-data metrics, real-robot success rates, ablations
• Evidence of top-tier work: publications (CoRL / RSS / ICRA / NeurIPS / ICML) or policies you shipped onto physical robots in production or serious field trials

Preferred

• Cross-embodiment training or action-space retargeting (EE-space representations, IK-aware pipelines)
• Teleoperation data collection systems; data-quality tooling for robot datasets
• Sim-to-real and simulation-based evaluation (Isaac, MuJoCo, Genesis)
• World models or video pre-training for robotics
• Japanese is not required — Zen operates in English